Spatial Visualization

Motivation

https://research.stlouisfed.org/publications/economic-synopses/2013/08/13/mind-the-regional-output-gap/

# Read combined statistical areas: https://www.census.gov/geo/maps-data/data/cbf/cbf_msa.html
cbsa <- combined_statistical_areas(cb = FALSE, resolution = "5m", year = 2017)

trying URL 'http://www2.census.gov/geo/tiger/TIGER2017/CSA/tl_2017_us_csa.zip'
Content type 'application/zip' length 11353157 bytes (10.8 MB)
==================================================
downloaded 10.8 MB

# states
states <- states(cb = FALSE, resolution = "5m", year = 2017)

trying URL 'http://www2.census.gov/geo/tiger/TIGER2017/STATE/tl_2017_us_state.zip'
Content type 'application/zip' length 9284383 bytes (8.9 MB)
==================================================
downloaded 8.9 MB

# state gdp (Bureau of Economic Advisors: https://www.bea.gov/regional/downloadzip.cfm)
state_gdp <- read_csv('QGSP_ALL_R.csv') %>% 
                filter(IndustryId == 1)

Parsed with column specification:
cols(
  .default = col_character(),
  Region = col_integer(),
  ComponentId = col_integer(),
  IndustryId = col_integer(),
  `2006Q1` = col_integer(),
  `2006Q2` = col_integer(),
  `2006Q3` = col_integer(),
  `2006Q4` = col_integer(),
  `2007Q4` = col_integer()
)
See spec(...) for full column specifications.
number of columns of result is not a multiple of vector length (arg 1)3 parsing failures.
row # A tibble: 3 x 5 col     row   col   expected    actual             file expected   <int> <chr>      <chr>     <chr>            <chr> actual 1  1441  <NA> 57 columns 1 columns 'QGSP_ALL_R.csv' file 2  1442  <NA> 57 columns 2 columns 'QGSP_ALL_R.csv' row 3  1443  <NA> 57 columns 1 columns 'QGSP_ALL_R.csv'

# convert Quarterly data to numeric
state_gdp[, grepl( "Q" , names( state_gdp ) ) ] <- state_gdp[, grepl( "Q" , names( state_gdp ) ) ] %>%
                lapply(function(x) as.numeric(as.character(x))) 

# plot the shapes to ensure we have the metro areas
leaflet(states) %>%
  addTiles() %>%
  addPolygons() %>%
  setMapWidgetStyle()

# fortify to convert into a dataframe
state_map <- fortify(states, region="GEOID")
# join "thematic" data
# gdpDF <- plyr::join(gdp, states@data, by = "GeoFIPS")

# plotting
gdp_state <- ggplot(state_gdp, aes(map_id='GeoFIPS')) +
  geom_map(aes(fill = '2016Q1'), map=state_map, color='#ffffff', size=0.1) + 
  expand_limits(x=state_map$long,y=state_map$lat)
gdp_state +
  coord_map("albers", lat0=30, lat1=40) +
  scale_fill_colormap("State RGDP",
                      colormap = colormaps$viridis, reverse = T, discrete = F)

Error: Discrete value supplied to continuous scale

leaflet(
  sf_NPR1to1,
  options= getLeafletOptions(-1.5, -1.5)) %>%
  addPolygons(
    weight=2,color='#000000', group = 'states',
    fillOpacity = 0.6, opacity = 1, fillColor= ~getFactorPal(state)(state),
    highlightOptions = highlightOptions(weight = 4)) %>%
  addLabelOnlyMarkers(
    data=sf_NPR1to1.centers,
    label = ~as.character(state),
    labelOptions = labelOptions(
    noHide = 'T', textOnly = T,
    offset=c(-4,-10), textsize = '12px')) %>%
  setMapWidgetStyle()

# From http://leafletjs.com/examples/choropleth/us-states.js
qpal <- colorQuantile("Blues", domain = state_gdp$'2016Q1', n = 7)
leaflet(states) %>%
  setView(-96, 37.8, 4) %>%
  addPolygons(
    color = ~qpal('2016Q1'),
    weight = 2,
    opacity = 1,
    dashArray = "3",
    fillOpacity = 0.7,
    smoothFactor = 0.2,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(pal = qpal, values = ~'2016Q1', opacity = 0.7, title = NULL,
    position = "bottomright")

Error in sum(sapply(label, function(x) { : 
  invalid 'type' (list) of argument